Continual Few-shot Relation Extraction (CFRE) is a practical problem that requires the model to continuously learn novel relations while avoiding forgetting old ones with few labeled training data. The primary challenges are catastrophic forgetting and overfitting. This paper harnesses prompt learning to explore the implicit capabilities of pre-trained language models to address the above two challenges, thereby making language models better continual few-shot relation extractors. Specifically, we propose a Contrastive Prompt Learning framework, which designs prompt representation to acquire more generalized knowledge that can be easily adapted to old and new categories, and margin-based contrastive learning to focus more on hard samples, therefore alleviating catastrophic forgetting and overfitting issues. To further remedy overfitting in low-resource scenarios, we introduce an effective memory augmentation strategy that employs well-crafted prompts to guide ChatGPT in generating diverse samples. Extensive experiments demonstrate that our method outperforms state-of-the-art methods by a large margin and significantly mitigates catastrophic forgetting and overfitting in low-resource scenarios.

使用Prompt Learning框架和对抗式学习等技术，本文提出了一种解决持续的少样本关系抽取中灾难性遗忘和过拟合问题的方法，该方法通过引入更广义的知识表示和关注难例样本实现了对预训练语言模型的扩展和优化，同时引入有效的内存增强策略来缓解在资源匮乏情况下的过拟合问题，并通过大量实验证明了该方法在性能上超过了现有方法，并且在资源匮乏情景下显著降低了灾难性遗忘和过拟合。

改进预训练语言模型以提升持续少样本关系抽取能力